Working Results2
Kaushik Narasimhan
Sys.Date()
Summary Statistics
Species Information
## y ymin ymax
## 1 0.4225771 0.3627116 0.4824426Species tables and tree tables
## Parsed with column specification:
## cols(
## Species = col_character(),
## `Scientific Name` = col_character(),
## `Func. Group` = col_character(),
## Sightings = col_double(),
## Ingestions = col_double(),
## Removals = col_double(),
## Nibbles = col_double(),
## `Avg. Vistitation Rate` = col_double(),
## `Avg. Fruit Removal Rate` = col_double(),
## SDE = col_double(),
## Class = col_character()
## )## [1] "Species" "Scientific Name"
## [3] "Func. Group" "Sightings"
## [5] "Ingestions" "Removals"
## [7] "Nibbles" "Avg. Vistitation Rate"
## [9] "Avg. Fruit Removal Rate" "SDE"
## [11] "Class"
## # A tibble: 2 x 4
## Height count mean sd
## <fct> <int> <dbl> <dbl>
## 1 high 25 0.301 0.387
## 2 low 45 0.0516 0.0580## TH Tree Height visits fruit.rem.rate SDE
## 1 258_low 258 low 0.36210317 0.150000000 0.054315476
## 2 258_high 258 high 1.01686508 0.524450549 0.533295450
## 3 13_low 13 low 1.25000000 0.153005464 0.191256831
## 4 13_high 13 high 0.77380952 0.634146341 0.490708479
## 5 18_low 18 low 0.20568783 0.006410256 0.001318512
## 6 79_low 79 low 0.76315438 0.153846154 0.117408366
## 7 79_high 79 high 2.00983045 0.559900109 1.125304287
## 8 250_high 250 high 0.00000000 0.000000000 0.000000000
## 9 388_high 388 high 1.59523810 0.806991774 1.287344021
## 10 388_low 388 low 0.51785714 0.054347826 0.028144410
## 11 406_low 406 low 1.31944444 0.098039216 0.129357298
## 12 406_high 406 high 0.62500000 0.571428571 0.357142857
## 13 200_low 200 low 1.51515152 0.115942029 0.175669741
## 14 203_low 203 low 2.17532468 0.066798523 0.145308476
## 15 6_high 6 high 0.19439935 0.702077922 0.136483492
## 16 6_low 6 low 0.11842324 0.080536312 0.009537371
## 17 75_low 75 low 0.15625000 0.000000000 0.000000000
## 18 203_high 203 high 0.25595238 0.731884058 0.187327467
## 19 90_high 90 high 0.42981902 0.561310976 0.241262135
## 20 205_low 205 low 0.24122807 0.136363636 0.032894737
## 21 25_high 25 high 0.03472222 1.000000000 0.034722222
## 22 41_low 41 low 0.00000000 0.000000000 0.000000000
## 23 41_high 41 high 0.28645833 0.714285714 0.204613095
## 24 92_low 92 low 0.28905508 0.070530733 0.020387267
## 25 67_high 67 high 0.37500000 1.000000000 0.375000000
## 26 262_low 262 low 0.06410256 0.000000000 0.000000000
## 27 293_high 293 high 0.00000000 0.000000000 0.000000000
## 28 8_low 8 low 0.07575758 0.000000000 0.000000000
## 29 19_low 19 low 0.00000000 0.000000000 0.000000000
## 30 144_low 144 low 0.64406318 0.177388836 0.114249618
## 31 160_high 160 high 0.00000000 0.000000000 0.000000000
## 32 90_low 90 low 0.09572218 0.190476190 0.018232796
## 33 138_high 138 high 0.35054200 0.456794294 0.160125586
## 34 138_low 138 low 0.32778922 0.258405694 0.084702600
## 35 127_low 127 low 0.07502914 0.104761905 0.007860195
## 36 127_high 127 high 0.06944444 0.750000000 0.052083333
## 37 60_low 60 low 0.00000000 0.000000000 0.000000000
## 38 82_high 82 high 1.64368964 0.769627660 1.265029014
## 39 82_low 82 low 0.72448385 0.253885148 0.183935689
## 40 107_low 107 low 0.06578947 0.666666667 0.043859649
## 41 121_low 121 low 0.05341880 0.046875000 0.002504006
## 42 121_high 121 high 0.00000000 0.000000000 0.000000000
## 43 141_low 141 low 0.30102710 0.256465517 0.077203070
## 44 160_low 160 low 0.49533800 0.106162431 0.052586286
## 45 23_low 23 low 0.04941239 0.175000000 0.008647169
## 46 399_high 399 high 0.23103632 0.159663866 0.036888153
## 47 399_low 399 low 0.23698524 0.303921569 0.072024925
## 48 84_low 84 low 0.20834691 0.120035703 0.025009067
## 49 134_low 134 low 0.07470539 0.128205128 0.009577614
## 50 384_low 384 low 0.24621212 0.416666667 0.102588384
## 51 84_high 84 high 0.00000000 0.000000000 0.000000000
## 52 197_high 197 high 0.72916667 0.565217391 0.412137681
## 53 197_low 197 low 0.78125000 0.026666667 0.020833333
## 54 46_low 46 low 0.45454545 0.064814815 0.029461279
## 55 53_low 53 low 0.11363636 0.000000000 0.000000000
## 56 129_high 129 high 0.00000000 0.000000000 0.000000000
## 57 129_low 129 low 0.57849702 0.090425532 0.052310901
## 58 17_low 17 low 0.41666667 0.070422535 0.029342723
## 59 54_low 54 low 0.23674242 0.169706180 0.040176653
## 60 89_low 89 low 0.07352941 0.120000000 0.008823529
## 61 295_high 295 high 0.96590909 0.424640400 0.410164023
## 62 295_low 295 low 0.48413826 0.225877193 0.109355791
## 63 83_low 83 low 1.10294118 0.133858268 0.147637795
## 64 92_high 92 high 0.19943020 0.358333333 0.071462488
## 65 97_low 97 low 0.05208333 0.000000000 0.000000000
## 66 269_low 269 low 0.06410256 0.500000000 0.032051282
## 67 26_low 26 low 0.00000000 0.000000000 0.000000000
## 68 72_low 72 low 0.49242424 0.288888889 0.142255892
## 69 72_high 72 high 0.29166667 0.500000000 0.145833333
## 70 265_low 265 low 0.00000000 0.000000000 0.000000000
##
## Kruskal-Wallis rank sum test
##
## data: SDE by Height
## Kruskal-Wallis chi-squared = 8.7213, df = 1, p-value = 0.003145## Df Sum Sq Mean Sq F value Pr(>F)
## Height 1 1.000 1.0005 18.15 6.43e-05 ***
## Residuals 68 3.748 0.0551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Loading required package: carData## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4##
## Attaching package: 'car'## The following object is masked from 'package:boot':
##
## logit## The following object is masked from 'package:psych':
##
## logit## The following object is masked from 'package:purrr':
##
## some## The following object is masked from 'package:dplyr':
##
## recode## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 19.494 3.705e-05 ***
## 68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Checking significance in differences of SDE
## Parsed with column specification:
## cols(
## Species = col_character(),
## `Scientific Name` = col_character(),
## `Func. Group` = col_character(),
## Sightings = col_double(),
## Ingestions = col_double(),
## Removals = col_double(),
## Nibbles = col_double(),
## `Avg. Vistitation Rate` = col_double(),
## `Avg. Fruit Removal Rate` = col_double(),
## SDE = col_double(),
## Class = col_character()
## )
## file saved to Table3.png## file saved to SPPtable.pdf## Note that HTML color may not be displayed on PDF properly.## [1] "FD" "NFD"## # A tibble: 2 x 4
## `Func. Group` count mean sd
## <fct> <int> <dbl> <dbl>
## 1 FD 7 2.14 2.05
## 2 NFD 13 0.446 0.454## # A tibble: 20 x 11
## Species `Scientific Nam… `Func. Group` Sightings Ingestions Removals Nibbles
## <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Centra… "Dasyprocta pun… NFD 260 0 44 70
## 2 Brown … "Metachirus nud… NFD 82 2 2 4
## 3 Baudó … "Penelope orton… FD 20 2 4 2
## 4 Choco … "Ramphastos bre… FD 272 99 142 3
## 5 Chestn… "Ramphastos amb… FD 316 157 168 1
## 6 South … "Nasua nasua " NFD 458 2 0 175
## 7 Collar… "Pecari tajacu" NFD 136 4 0 0
## 8 Kinkaj… "Potos flavus" NFD 84 0 3 14
## 9 Oilbird "Steatornis car… FD 73 0 36 0
## 10 Common… "Didelphis mars… NFD 100 1 0 27
## 11 Lowlan… "Cuniculus paca" NFD 416 0 31 73
## 12 Rufous… "Odontophorus e… NFD 434 0 0 4
## 13 Rodent… "" NFD 1380 0 197 45
## 14 Rufous… "Diplomys labil… NFD 5 0 0 1
## 15 Southe… "Amazona farino… FD 10 0 7 0
## 16 Squirr… "" NFD 675 0 249 109
## 17 Toucan… "" FD 284 75 85 0
## 18 Tome's… "Proechimys sem… NFD 136 0 24 1
## 19 Long-w… "Cephalopterus … FD 269 34 96 2
## 20 Brown … "Aramides wolfi" NFD 127 0 0 7
## # … with 4 more variables: `Avg. Vistitation Rate` <dbl>, `Avg. Fruit Removal
## # Rate` <dbl>, SDE <dbl>, Class <chr>
##
## One-way analysis of means (not assuming equal variances)
##
## data: SDE and Func_group
## F = 4.641, num df = 1.0000, denom df = 6.3171, p-value = 0.07238##
## Kruskal-Wallis rank sum test
##
## data: SDE by Func_group
## Kruskal-Wallis chi-squared = 5.3009, df = 1, p-value = 0.02131## Df Sum Sq Mean Sq F value Pr(>F)
## Func_group 1 13.05 13.054 8.462 0.00936 **
## Residuals 18 27.77 1.543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SDE ~ Func_group, data = spptable)
##
## $Func_group
## diff lwr upr p adj
## NFD-FD -1.693846 -2.917178 -0.4705144 0.0093621## Df Sum Sq Mean Sq F value Pr(>F)
## Class 1 6.40 6.401 3.347 0.0839 .
## Residuals 18 34.42 1.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SDE ~ Class, data = spptable)
##
## $Class
## diff lwr upr p adj
## Mammal-Bird -1.137172 -2.443006 0.1686627 0.0839294## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 6.2905 0.02194 *
## 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 5.2885 0.03365 *
## 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Parsed with column specification:
## cols(
## Species = col_character(),
## `Scientific Name` = col_character(),
## `Func. Group` = col_character(),
## Sightings = col_double(),
## Ingestions = col_double(),
## Removals = col_double(),
## Nibbles = col_double(),
## `Avg. Vistitation Rate` = col_double(),
## `Avg. Fruit Removal Rate` = col_double(),
## SDE = col_double(),
## Class = col_character()
## )Species | Scientific Name | Func. Group | Sightings | Ingestions | Removals | Nibbles | Avg. Vistitation Rate | Avg. Fruit Removal Rate |
Central American agouti | Dasyprocta punctata | NFD | 260 | 0 | 44 | 70 | 0.21 | 5.50 |
Brown four-eyed opossum | Metachirus nudicaudatus | NFD | 82 | 2 | 2 | 4 | 0.10 | 1.33 |
Baudó guan | Penelope ortoni | FD | 20 | 2 | 4 | 2 | 0.09 | 1.50 |
Choco toucan | Ramphastos brevis | FD | 272 | 99 | 142 | 3 | 0.34 | 12.68 |
Chestnut-mandibled toucan | Ramphastos ambiguus swainsonii | FD | 316 | 157 | 168 | 1 | 0.38 | 14.77 |
South American coati | Nasua nasua | NFD | 458 | 2 | 0 | 175 | 0.28 | 1.00 |
Collared peccary | Pecari tajacu | NFD | 136 | 4 | 0 | 0 | 0.10 | 4.00 |
Kinkajou | Potos flavus | NFD | 84 | 0 | 3 | 14 | 0.15 | 1.00 |
Oilbird | Steatornis caripensis | FD | 73 | 0 | 36 | 0 | 0.21 | 5.14 |
Common opossum | Didelphis marsupialis | NFD | 100 | 1 | 0 | 27 | 0.18 | 1.00 |
Lowland paca | Cuniculus paca | NFD | 416 | 0 | 31 | 73 | 0.23 | 3.88 |
Rufous fronted wood-quail | Odontophorus erythrops | NFD | 434 | 0 | 0 | 4 | 0.08 | 0.00 |
Rodent spp. | NFD | 1380 | 0 | 197 | 45 | 0.17 | 4.48 | |
Rufous soft-furred spiny-rat | Diplomys labilis | NFD | 5 | 0 | 0 | 1 | 0.05 | 0.00 |
Southern mealy parrot | Amazona farinosa | FD | 10 | 0 | 7 | 0 | 0.21 | 7.00 |
Squirrel spp. | NFD | 675 | 0 | 249 | 109 | 0.23 | 5.79 | |
Toucan spp. | FD | 284 | 75 | 85 | 0 | 0.22 | 8.42 | |
Tome's spiny-rat | Proechimys semispinosus | NFD | 136 | 0 | 24 | 1 | 0.13 | 4.00 |
Long-wattled umbrellabird | Cephalopterus penduliger | FD | 269 | 34 | 96 | 2 | 0.13 | 3.94 |
Brown wood-rail | Aramides wolfi | NFD | 127 | 0 | 0 | 7 | 0.15 | 0.00 |
Visualization of ht vs focal 50
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Correlation at each band
effect of height focal against visitation
## Tree Height Month Year Species utm1 utm2 ele Visitation
## 16 79 high 10 2016 Oenocarpus bataua 644127 38676 540 189
## Richness M.Richness B.Richness T.Ingestions T.Removal T.Nibble
## 16 5 0 5 57 117 0
## Avg.synch.neighbors Lek. DAP_CENSUS_1 ALTURA_CENSUS_1 NOTAS_CENSUS_1
## 16 NA LEK1 29.5 21 NA
## TIPO_DE_BOSQUE_COLLECTION DOSEL_CENSUS_1 CANOPY_DENS_CENSUS_1
## 16 Secundario 21 91.42
## ARB_DAP10_CENSUS_1 ARB_DAP50_CENSUS_1 CERCROPIA_CENSUS_1 MICONIA_CENSUS_1
## 16 9 0 0 0
## JUV_CENSUS_1 JUV_DENS_CENSUS_1 PLANTULA_CENSUS_1 PLANTULA_DENS_CENSUS_1
## 16 4 0.05092958 41 2.088113
## m.visitation b.visitation real.visitation real.m.visitation
## 16 0 189 189 0
## real.b.visitation real.richness real.m.richness real.b.richness date
## 16 189 5 0 5 42644
## focalmonth..50 focalmonth..100 focalmonth..150 focalmonth..200
## 16 2 3 6 9
## focalmonth..250 focalmonth..300 focalmonth..350 focalmonth..400
## 16 10 12 15 16
## focalmonth..450 focalmonth..500 TD FD NRFM NFM NRFG NFG start end
## 16 16 16 0 189 1 d 1 e 10/5/16 10/16/16
## days vis.rate n50 n100 n150 n200 n250 n300 n350 n400 n450 n500 Dates
## 16 11 17.18182 5 15 37 61 81 114 140 152 160 173 2016-10-01
## FD.rate TD.rate
## 16 17.18182 0
Models 50 m band
Models using functional groups
Model Comparisons
Model 1: Original
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0405307 | 0.1886986 | 0.2147905 | 0.8299306 |
| focalmonth..50 | 0.5152201 | 0.2956979 | 1.7423871 | 0.0814407 |
| Heightlow | 0.7238617 | 0.0437088 | 16.5610200 | 0.0000000 |
| focalmonth..50:Heightlow | -0.2752461 | 0.0374950 | -7.3408839 | 0.0000000 |
## real.visitation ~ focalmonth..450 * Height + offset(lograte) +
## (1 + focalmonth..450 | Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.2234468 | 0.3520308 | 0.6347365 | 0.5256003 |
| focalmonth..450 | -0.0047342 | 0.0302651 | -0.1564254 | 0.8756977 |
| Heightlow | 0.4050716 | 0.0852282 | 4.7527900 | 0.0000020 |
| focalmonth..450:Heightlow | 0.0042980 | 0.0059529 | 0.7220041 | 0.4702920 |
## Parsed with column specification:
## cols(
## X1 = col_character(),
## `z/tau value` = col_double(),
## `±SE` = col_double(),
## `p value` = col_double()
## )
## file saved to TableGLMM2.pdfModel 2: Trees without any visits from Dispersers are removed
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0415496 | 0.1880433 | 0.2209575 | 0.8251255 |
| focalmonth..50 | 0.5152245 | 0.2960341 | 1.7404230 | 0.0817848 |
| Heightlow | 0.7235774 | 0.0436765 | 16.5667539 | 0.0000000 |
| focalmonth..50:Heightlow | -0.2750984 | 0.0374846 | -7.3389674 | 0.0000000 |
Results are qualitatively similar.
Model 3: Original amount of trees, but fruiting neighborhood is now binary
## real.visitation ~ bin50 * Height + offset(lograte) + (1 + bin50 |
## Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0439227 | 0.1998483 | 0.219780 | 0.8260425 |
| bin501 | 0.5713877 | 0.3322725 | 1.719636 | 0.0854987 |
| Heightlow | 0.6624992 | 0.0483892 | 13.691042 | 0.0000000 |
| bin501:Heightlow | -0.3084512 | 0.0692597 | -4.453547 | 0.0000084 |
Again, binarizing is qualitatively similar.
Model 4: Original amount of trees, but fruiting neighborhood categorized into 0,1,2,3+
## real.visitation ~ bin50 * Height + offset(lograte) + (1 | Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.1688387 | 0.1767227 | 0.9553876 | 0.3393817 |
| bin50(1) | 0.1033138 | 0.0840268 | 1.2295336 | 0.2188718 |
| bin50(2) | 0.0176622 | 0.1071663 | 0.1648108 | 0.8690929 |
| bin50(3) | -0.4130312 | 0.1485860 | -2.7797440 | 0.0054402 |
| Heightlow | 0.6869025 | 0.0479722 | 14.3187591 | 0.0000000 |
| bin50(1):Heightlow | -0.2540545 | 0.0754133 | -3.3688264 | 0.0007549 |
| bin50(2):Heightlow | -0.8947907 | 0.1114449 | -8.0289979 | 0.0000000 |
| bin50(3):Heightlow | -0.2842984 | 0.1605031 | -1.7712948 | 0.0765117 |
Model 5: Original data but tree with outlier in visitation in high cameras removed
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0650785 | 0.1908849 | 0.3409307 | 0.7331558 |
| focalmonth..50 | 0.3663191 | 0.3096886 | 1.1828626 | 0.2368636 |
| Heightlow | 0.6933769 | 0.0439534 | 15.7752735 | 0.0000000 |
| focalmonth..50:Heightlow | -0.0700859 | 0.0400875 | -1.7483212 | 0.0804084 |
Focalmonth..50 and the interaction term are no longer significant.
Model 6: Trees without any visits from Dispersers are removed AND outlier in visitation in high cameras removed
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0657147 | 0.1905568 | 0.3448563 | 0.7302024 |
| focalmonth..50 | 0.3664713 | 0.3096947 | 1.1833307 | 0.2366781 |
| Heightlow | 0.6931354 | 0.0439257 | 15.7797227 | 0.0000000 |
| focalmonth..50:Heightlow | -0.0699615 | 0.0400800 | -1.7455466 | 0.0808898 |
Results are qualitatively similar again when trees with 0 disperser visitation is removed
Checking SDE on the tree level
## file saved to euptcvm.pdfNetwork Analysis
## specialisation asymmetry H2
## 0.1895788 0.4342014## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL
## 0.8235261 0.9700709
## generality.HL vulnerability.LL
## 12.9410832 3.5444669
## specialisation asymmetry H2
## 0.09440097 0.35740596## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL
## 0.7975048 0.9144969
## generality.HL vulnerability.LL
## 6.0560145 3.7946460Correlation between Fruiting neighborhood (FN) at 50m and for FN at whole study plot
##
## Shapiro-Wilk normality test
##
## data: dummy$fn1400
## W = 0.93961, p-value = 4.615e-06##
## Shapiro-Wilk normality test
##
## data: dummy$fn50
## W = 0.69195, p-value < 2.2e-16| statistic | p.value | kendall_score | denominator | var_kendall_score |
|---|---|---|---|---|
| 0.1360697 | 0.0378435 | 1118 | 8216.378 | 289349.8 |
Model 1: total rate ~ fn50 + FNTotal + Camera height
Model 2: Fd rate ~ fn50 + FNTotal + Camera height
Model 3: Non Flying d rate ~ fn50 + FNTotal + Camera height
## Family: poisson ( log )
## Formula: FD.V ~ fn50 + Height + fn1400 + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 144
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 2.068 1.438
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.71708 0.15653 -4.58 4.62e-06 ***
## fn50 -0.41439 0.10446 -3.97 7.27e-05 ***
## Heightlow -1.97636 0.03695 -53.49 < 2e-16 ***
## fn1400 0.07405 0.01093 6.77 1.25e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02337 0.07064 -0.331 0.7408
## fn50 0.34464 0.17642 1.954 0.0508 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula:
## FD.V ~ fn50 * Heightlf + fn1400 + offset(lograte) + (1 | Tree)
## Zero inflation: ~1
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 144
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 1.909 1.382
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.16099 0.07589 -41.65 <2e-16 ***
## fn50 -2.21497 NA NA NA
## Heightlfhigh 1.94215 NA NA NA
## fn1400 0.06695 NA NA NA
## fn50:Heightlfhigh 1.86121 NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0296 NA NA NA| Â | Visitation rate with random intercept | Flying visitation rate with random intercept | Non-Flying visitation rate with random intercept | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.61 | 0.45 – 0.82 | 0.001 | 0.49 | 0.36 – 0.66 | <0.001 | 0.58 | 0.33 – 0.99 | 0.047 |
| fn50 | 0.85 | 0.78 – 0.92 | <0.001 | 0.66 | 0.54 – 0.81 | <0.001 | 0.93 | 0.83 – 1.04 | 0.194 |
| Height [low] | 0.58 | 0.53 – 0.65 | <0.001 | 0.14 | 0.13 – 0.15 | <0.001 | 1.26 | 1.00 – 1.59 | 0.051 |
| fn1400 | 1.04 | 1.03 – 1.05 | <0.001 | 1.08 | 1.05 – 1.10 | <0.001 | 1.00 | 0.98 – 1.02 | 0.882 |
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.36 | 0.23 – 0.56 | <0.001 | 0.98 | 0.85 – 1.12 | 0.741 | 0.94 | 0.64 – 1.40 | 0.767 |
| fn50 | 0.83 | 0.55 – 1.26 | 0.387 | 1.41 | 1.00 – 1.99 | 0.051 | 0.80 | 0.57 – 1.12 | 0.189 |
| Random Effects | |||||||||
| σ2 | 0.71 | 1.80 | 0.54 | ||||||
| Ï„00 | 0.78 Tree | 2.07 Tree | 0.69 Tree | ||||||
| ICC | 0.52 | 0.54 | 0.56 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.094 / 0.568 | 0.257 / 0.655 | 0.014 / 0.565 | ||||||
| Estimate | Estimate | Estimate | |
|---|---|---|---|
| (Intercept) | -0.4931816 | -0.7170830 | -0.5527095 |
| fn50 | -0.1651333 | -0.4143911 | -0.0726639 |
| Heightlow | -0.5404367 | -1.9763615 | 0.2316179 |
| fn1400 | 0.0399221 | 0.0740467 | 0.0016721 |
Model 3.5 Models
## Family: poisson ( log )
## Formula:
## total.V ~ fn50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 1966.5 1990.7 -975.3 1950.5 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.778 0.882
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.924e-01 1.867e-01 -2.638 0.00834 **
## fn50 -1.662e-01 1.570e-01 -1.059 0.28964
## fn1400 3.990e-02 7.766e-03 5.138 2.78e-07 ***
## Heightlow -5.404e-01 5.240e-02 -10.313 < 2e-16 ***
## fn50:fn1400 5.275e-05 7.173e-03 0.007 0.99413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0242 0.2246 -4.560 5.12e-06 ***
## fn50 -0.1824 0.2141 -0.852 0.394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula: FD.V ~ fn50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 967.2 991.3 -475.6 951.2 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 1.55 1.245
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.80919 0.28804 -2.809 0.00497 **
## fn50 -1.02918 0.26078 -3.947 7.93e-05 ***
## fn1400 0.05025 0.01291 3.894 9.87e-05 ***
## Heightlow -1.97516 0.22285 -8.863 < 2e-16 ***
## fn50:fn1400 0.03195 0.01158 2.760 0.00579 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1271 0.2412 0.527 0.598
## fn50 0.2714 0.2235 1.214 0.225
## Family: poisson ( log )
## Formula: TD.V ~ fn50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 982.3 1006.5 -483.2 966.3 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.7233 0.8505
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.75049 0.30000 -2.502 0.0124 *
## fn50 0.61528 0.30310 2.030 0.0424 *
## fn1400 0.01215 0.01217 0.998 0.3181
## Heightlow 0.19858 0.11980 1.658 0.0974 .
## fn50:fn1400 -0.03165 0.01368 -2.313 0.0207 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.06295 0.20202 -0.312 0.755
## fn50 -0.21965 0.17130 -1.282 0.200## Family: poisson ( log )
## Formula:
## total.V ~ fn100 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn100
## Data: dummy_2
##
## AIC BIC logLik deviance df.resid
## 1947.5 1971.7 -965.8 1931.5 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.6816 0.8256
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.670075 0.202232 -3.313 0.000922 ***
## fn100 0.070317 0.094904 0.741 0.458740
## fn1400 0.057178 0.008713 6.563 5.29e-11 ***
## Heightlow -0.525584 0.052524 -10.007 < 2e-16 ***
## fn100:fn1400 -0.009133 0.004302 -2.123 0.033760 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.98562 0.25994 -3.792 0.00015 ***
## fn100 -0.06931 0.11048 -0.627 0.53042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula: FD.V ~ fn100 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn100
## Data: dummy_2
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 1.484 1.218
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.850580 0.149165 -5.702 1.18e-08 ***
## fn100 -0.270153 0.131115 -2.060 0.0394 *
## fn1400 0.078796 NA NA NA
## Heightlow -1.782240 0.160866 -11.079 < 2e-16 ***
## fn100:fn1400 0.002474 0.005009 0.494 0.6213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.15277 NA NA NA
## fn100 0.15050 0.08365 1.799 0.072 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Family: poisson ( log )
## Formula: TD.V ~ fn100 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn100
## Data: dummy_2
##
## AIC BIC logLik deviance df.resid
## 978.0 1002.1 -481.0 962.0 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.7306 0.8547
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.891883 0.307413 -2.901 0.00372 **
## fn100 0.331256 0.140098 2.365 0.01806 *
## fn1400 0.024407 0.013150 1.856 0.06345 .
## Heightlow 0.204940 0.119029 1.722 0.08511 .
## fn100:fn1400 -0.019103 0.006679 -2.860 0.00423 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02677 0.23236 -0.115 0.908
## fn100 -0.10353 0.09335 -1.109 0.267| Â | Visitation rate with random intercept | Visitation rate with random intercept AND SLOPE | Flying visitation rate with random intercept | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.51 | 0.34 – 0.76 | 0.001 | 0.43 | 0.32 – 0.57 | <0.001 | 0.41 | 0.22 – 0.75 | 0.004 |
| fn100 | 1.07 | 0.89 – 1.29 | 0.459 | 0.76 | 0.59 – 0.99 | 0.039 | 1.39 | 1.06 – 1.83 | 0.018 |
| fn1400 | 1.06 | 1.04 – 1.08 | <0.001 | 1.08 | NaN – NaN | NaN | 1.02 | 1.00 – 1.05 | 0.063 |
| Height [low] | 0.59 | 0.53 – 0.66 | <0.001 | 0.17 | 0.12 – 0.23 | <0.001 | 1.23 | 0.97 – 1.55 | 0.085 |
| fn100 * fn1400 | 0.99 | 0.98 – 1.00 | 0.034 | 1.00 | 0.99 – 1.01 | 0.621 | 0.98 | 0.97 – 0.99 | 0.004 |
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.37 | 0.22 – 0.62 | <0.001 | 1.17 | NaN – NaN | NaN | 0.97 | 0.62 – 1.54 | 0.908 |
| fn100 | 0.93 | 0.75 – 1.16 | 0.530 | 1.16 | 0.99 – 1.37 | 0.072 | 0.90 | 0.75 – 1.08 | 0.267 |
| Random Effects | |||||||||
| σ2 | 0.72 | 1.71 | 0.56 | ||||||
| Ï„00 | 0.68 Tree | 1.48 Tree | 0.73 Tree | ||||||
| ICC | 0.49 | 0.47 | 0.57 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.114 / 0.544 | 0.272 / 0.611 | 0.031 / 0.579 | ||||||
| AIC | 1947.520 | NA | 978.002 | ||||||
| Â | Visitation rate with random intercept | Visitation rate with random intercept AND SLOPE | Flying visitation rate with random intercept | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.61 | 0.42 – 0.88 | 0.008 | 0.45 | 0.25 – 0.78 | 0.005 | 0.47 | 0.26 – 0.85 | 0.012 |
| fn50 | 0.85 | 0.62 – 1.15 | 0.290 | 0.36 | 0.21 – 0.60 | <0.001 | 1.85 | 1.02 – 3.35 | 0.042 |
| fn1400 | 1.04 | 1.02 – 1.06 | <0.001 | 1.05 | 1.03 – 1.08 | <0.001 | 1.01 | 0.99 – 1.04 | 0.318 |
| Height [low] | 0.58 | 0.53 – 0.65 | <0.001 | 0.14 | 0.09 – 0.21 | <0.001 | 1.22 | 0.96 – 1.54 | 0.097 |
| fn50 * fn1400 | 1.00 | 0.99 – 1.01 | 0.994 | 1.03 | 1.01 – 1.06 | 0.006 | 0.97 | 0.94 – 1.00 | 0.021 |
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.36 | 0.23 – 0.56 | <0.001 | 1.14 | 0.71 – 1.82 | 0.598 | 0.94 | 0.63 – 1.40 | 0.755 |
| fn50 | 0.83 | 0.55 – 1.27 | 0.394 | 1.31 | 0.85 – 2.03 | 0.225 | 0.80 | 0.57 – 1.12 | 0.200 |
| Random Effects | |||||||||
| σ2 | 0.71 | 1.70 | 0.55 | ||||||
| Ï„00 | 0.78 Tree | 1.55 Tree | 0.72 Tree | ||||||
| ICC | 0.52 | 0.48 | 0.57 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.094 / 0.567 | 0.287 / 0.628 | 0.025 / 0.576 | ||||||
| AIC | 1966.533 | 967.186 | 982.329 | ||||||
| Estimate | Estimate | Estimate | |
|---|---|---|---|
| (Intercept) | -0.4924035 | -0.8091924 | -0.7504906 |
| fn50 | -0.1662483 | -1.0291770 | 0.6152846 |
| fn1400 | 0.0398962 | 0.0502546 | 0.0121530 |
| Heightlow | -0.5404348 | -1.9751555 | 0.1985775 |
| fn50:fn1400 | 0.0000527 | 0.0319515 | -0.0316486 |
Model 4: All models including crop size but no interaction terms
## Family: poisson ( log )
## Formula:
## total.V ~ fn50 + Height + fn1400 + cropsize + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 1901.7 1934.9 -939.8 1879.7 140
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.7214 0.8494
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.563966 0.236021 -2.389 0.01687 *
## fn50 -0.120211 0.043809 -2.744 0.00607 **
## Heightlow -0.595176 0.054561 -10.908 < 2e-16 ***
## fn1400 0.036728 0.008522 4.310 1.63e-05 ***
## cropsize.L 0.166032 0.254977 0.651 0.51494
## cropsize.Q -0.442950 0.222020 -1.995 0.04603 *
## cropsize.C 0.014846 0.183665 0.081 0.93557
## cropsize^4 0.512372 0.111204 4.608 4.08e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0026 0.2250 -4.455 8.38e-06 ***
## fn50 -0.1581 0.2043 -0.774 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: dummy
## Models:
## m2: total.V ~ fn50 + Height + fn1400 + (1 | Tree) + offset(lograte), zi=~fn50, disp=~1
## model.1RIcrop: total.V ~ fn50 + Height + fn1400 + cropsize + offset(lograte) + , zi=~fn50, disp=~1
## model.1RIcrop: (1 | Tree), zi=~fn50, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2 7 1964.5 1985.7 -975.27 1950.5
## model.1RIcrop 11 1901.7 1934.9 -939.85 1879.7 70.837 4 1.511e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.562
## Marginal R2: 0.127## fn50 Height fn1400 cropsize emmean SE df lower.CL upper.CL
## 0.662 high 17.3 a 2.17 0.430 140 1.322 3.02
## 0.662 low 17.3 a 1.58 0.426 140 0.735 2.42
## 0.662 high 17.3 b 2.29 0.240 140 1.814 2.76
## 0.662 low 17.3 b 1.69 0.244 140 1.209 2.18
## 0.662 high 17.3 c 3.06 0.173 140 2.720 3.40
## 0.662 low 17.3 c 2.47 0.169 140 2.132 2.80
## 0.662 high 17.3 d 2.37 0.172 140 2.034 2.71
## 0.662 low 17.3 d 1.78 0.171 140 1.440 2.12
## 0.662 high 17.3 e 2.39 0.179 140 2.038 2.74
## 0.662 low 17.3 e 1.80 0.181 140 1.438 2.16
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
## Family: poisson ( log )
## Formula: FD.V ~ fn50 + Height + fn1400 + cropsize + offset(lograte) +
## (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 140
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 2.005 1.416
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.142008 0.100966 -11.31 < 2e-16 ***
## fn50 -0.351001 0.078046 -4.50 6.88e-06 ***
## Heightlow -2.389815 0.194816 -12.27 < 2e-16 ***
## fn1400 0.027412 0.003297 8.31 < 2e-16 ***
## cropsize.L 3.122236 0.264788 11.79 < 2e-16 ***
## cropsize.Q -2.426704 0.030621 -79.25 < 2e-16 ***
## cropsize.C 2.091143 0.143001 14.62 < 2e-16 ***
## cropsize^4 -0.539855 0.163815 -3.30 0.000982 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1757 0.2565 -0.685 0.493
## fn50 0.4473 0.1063 4.206 2.6e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Not enough model terms in the zero_inflated part of the model to check for multicollinearity.## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Data: dummy
## Models:
## model.2crop: FD.V ~ fn50 + Height + fn1400 + cropsize + offset(lograte) + , zi=~fn50, disp=~1
## model.2crop: (1 | Tree), zi=~fn50, disp=~1
## m2: FD.V ~ fn50 + Height + fn1400 + cropsize + (1 | Tree) + offset(lograte), zi=~fn50, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model.2crop 11
## m2 11 0
## Data: dummy
## Models:
## model.3crop: TD.V ~ fn50 + Height + fn1400 + cropsize + offset(lograte) + , zi=~fn50, disp=~1
## model.3crop: (1 | Tree), zi=~fn50, disp=~1
## m2: TD.V ~ fn50 + Height + fn1400 + cropsize + (1 | Tree) + offset(lograte), zi=~fn50, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model.3crop 11 930.3 963.49 -454.15 908.3
## m2 11 930.3 963.49 -454.15 908.3 0 0 1
| Â | total.V | FD.V | TD.V | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.57 | 0.36 – 0.90 | 0.017 | 0.32 | 0.26 – 0.39 | <0.001 | 0.30 | 0.15 – 0.62 | 0.001 |
| fn50 | 0.89 | 0.81 – 0.97 | 0.006 | 0.70 | 0.60 – 0.82 | <0.001 | 0.88 | 0.78 – 0.98 | 0.022 |
| Height [low] | 0.55 | 0.50 – 0.61 | <0.001 | 0.09 | 0.06 – 0.13 | <0.001 | 1.40 | 1.09 – 1.80 | 0.009 |
| fn1400 | 1.04 | 1.02 – 1.05 | <0.001 | 1.03 | 1.02 – 1.03 | <0.001 | 1.00 | 0.98 – 1.03 | 0.792 |
| cropsize.L | 1.18 | 0.72 – 1.95 | 0.515 | 22.70 | 13.51 – 38.14 | <0.001 | 2.65 | 1.35 – 5.20 | 0.005 |
| cropsize.Q | 0.64 | 0.42 – 0.99 | 0.046 | 0.09 | 0.08 – 0.09 | <0.001 | 0.78 | 0.43 – 1.41 | 0.403 |
| cropsize.C | 1.01 | 0.71 – 1.45 | 0.936 | 8.09 | 6.12 – 10.71 | <0.001 | 0.52 | 0.19 – 1.40 | 0.194 |
| cropsize^4 | 1.67 | 1.34 – 2.08 | <0.001 | 0.58 | 0.42 – 0.80 | 0.001 | 3.50 | 1.72 – 7.12 | 0.001 |
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.37 | 0.24 – 0.57 | <0.001 | 0.84 | 0.51 – 1.39 | 0.493 | 0.87 | 0.52 – 1.47 | 0.611 |
| fn50 | 0.85 | 0.57 – 1.27 | 0.439 | 1.56 | 1.27 – 1.93 | <0.001 | 0.82 | 0.56 – 1.18 | 0.280 |
| Random Effects | |||||||||
| σ2 | 0.73 | 2.05 | 0.51 | ||||||
| Ï„00 | 0.72 Tree | 2.00 Tree | 0.63 Tree | ||||||
| ICC | 0.50 | 0.49 | 0.55 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.127 / 0.562 | 0.321 / 0.656 | 0.197 / 0.638 | ||||||
Model 5: cropsize again but binarized with low being below 500
## Family: poisson ( log )
## Formula:
## total.V ~ fn50 + Height + fn1400 + csizeb + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 1920.2 1944.3 -952.1 1904.2 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.7294 0.854
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.596084 0.207320 -2.875 0.00404 **
## fn50 -0.120066 0.044043 -2.726 0.00641 **
## Heightlow -0.569945 0.053448 -10.664 < 2e-16 ***
## fn1400 0.035523 0.007259 4.893 9.91e-07 ***
## csizebLow 0.508652 0.073704 6.901 5.15e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0017 0.2234 -4.484 7.32e-06 ***
## fn50 -0.1551 0.2037 -0.761 0.447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula:
## FD.V ~ fn50 + Height + fn1400 + csizeb + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 972.6 996.7 -478.3 956.6 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 1.563 1.25
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.07453 0.28910 -3.717 0.000202 ***
## fn50 -0.32242 0.08240 -3.913 9.12e-05 ***
## Heightlow -2.24234 0.26167 -8.569 < 2e-16 ***
## fn1400 0.06250 0.01169 5.344 9.08e-08 ***
## csizebLow 0.19925 0.12580 1.584 0.113234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06932 0.25576 0.271 0.786
## fn50 0.28032 0.22452 1.248 0.212## Family: tweedie ( log )
## Formula:
## TD.V ~ fn50 + Height + fn1400 + csizeb + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 748.0 778.2 -364.0 728.0 141
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.4007 0.633
## Number of obs: 151, groups: Tree, 47
##
## Overdispersion parameter for tweedie family (): 4.89
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.22212 0.48325 -6.668 2.60e-11 ***
## fn50 -0.03436 0.12926 -0.266 0.7904
## Heightlow 1.76597 0.27139 6.507 7.66e-11 ***
## fn1400 0.04854 0.02040 2.379 0.0174 *
## csizebLow 0.47916 0.25973 1.845 0.0651 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -19.320 5643.465 -0.003 0.997
## fn50 -2.112 14674.518 0.000 1.000
Model 6: no interaction terms
## Family: poisson ( log )
## Formula:
## total.V ~ fn50 + fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 1964.5 1985.7 -975.3 1950.5 144
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.7784 0.8823
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.493182 0.152912 -3.225 0.00126 **
## fn50 -0.165133 0.041331 -3.995 6.46e-05 ***
## fn1400 0.039922 0.006913 5.775 7.72e-09 ***
## Heightlow -0.540437 0.052399 -10.314 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0242 0.2246 -4.559 5.13e-06 ***
## fn50 -0.1827 0.2114 -0.864 0.387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula: FD.V ~ fn50 + fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 144
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 2.068 1.438
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.71708 0.15653 -4.58 4.62e-06 ***
## fn50 -0.41439 0.10446 -3.97 7.27e-05 ***
## fn1400 0.07405 0.01093 6.77 1.25e-11 ***
## Heightlow -1.97636 0.03695 -53.49 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02337 0.07063 -0.331 0.7408
## fn50 0.34464 0.17642 1.954 0.0508 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula: TD.V ~ fn50 + fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 985.7 1006.8 -485.8 971.7 144
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.6887 0.8299
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.552710 0.277812 -1.990 0.0466 *
## fn50 -0.072664 0.055958 -1.298 0.1941
## fn1400 0.001672 0.011223 0.149 0.8816
## Heightlow 0.231618 0.118685 1.952 0.0510 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.05933 0.20061 -0.296 0.767
## fn50 -0.22521 0.17152 -1.313 0.189| Â | total.V | FD.V | TD.V | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.61 | 0.45 – 0.82 | 0.001 | 0.49 | 0.36 – 0.66 | <0.001 | 0.58 | 0.33 – 0.99 | 0.047 |
| fn50 | 0.85 | 0.78 – 0.92 | <0.001 | 0.66 | 0.54 – 0.81 | <0.001 | 0.93 | 0.83 – 1.04 | 0.194 |
| fn1400 | 1.04 | 1.03 – 1.05 | <0.001 | 1.08 | 1.05 – 1.10 | <0.001 | 1.00 | 0.98 – 1.02 | 0.882 |
| Height [low] | 0.58 | 0.53 – 0.65 | <0.001 | 0.14 | 0.13 – 0.15 | <0.001 | 1.26 | 1.00 – 1.59 | 0.051 |
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.36 | 0.23 – 0.56 | <0.001 | 0.98 | 0.85 – 1.12 | 0.741 | 0.94 | 0.64 – 1.40 | 0.767 |
| fn50 | 0.83 | 0.55 – 1.26 | 0.387 | 1.41 | 1.00 – 1.99 | 0.051 | 0.80 | 0.57 – 1.12 | 0.189 |
| Random Effects | |||||||||
| σ2 | 0.71 | 1.80 | 0.54 | ||||||
| Ï„00 | 0.78 Tree | 2.07 Tree | 0.69 Tree | ||||||
| ICC | 0.52 | 0.54 | 0.56 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.094 / 0.568 | 0.257 / 0.655 | 0.014 / 0.565 | ||||||
Model 7: including environmental variables
## Family: poisson ( log )
## Formula:
## total.V ~ fn50 + fn1400 + Height + CanopyDens + cec + offset(lograte) +
## (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 1966.9 1994.0 -974.4 1948.9 142
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.7588 0.8711
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.791191 1.022065 0.774 0.439
## fn50 -0.166319 0.042176 -3.943 8.03e-05 ***
## fn1400 0.040068 0.007046 5.687 1.30e-08 ***
## Heightlow -0.539353 0.052436 -10.286 < 2e-16 ***
## CanopyDens -0.013846 0.011645 -1.189 0.234
## cec -0.186658 0.230268 -0.811 0.418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0402 0.2312 -4.50 6.81e-06 ***
## fn50 -0.1707 0.2189 -0.78 0.435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Family: poisson ( log )
## Formula:
## FD.V ~ fn50 + fn1400 + Height + CanopyDens + offset(lograte) + (1 | Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## NA NA NA NA 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 1.001 1
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0001085 0.1861159 -0.001 1.000
## fn50 0.0003087 0.0717271 0.004 0.997
## fn1400 0.0027676 0.0114309 0.242 0.809
## Heightlow -0.0017971 0.1295614 -0.014 0.989
## CanopyDens -0.0101341 0.0021772 -4.655 3.25e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0002574 0.0590400 0.004 0.997
## fn50 0.0002967 0.1089829 0.003 0.998## Family: poisson ( log )
## Formula: TD.V ~ fn50 + fn1400 + Height + cec + offset(lograte) + (1 |
## Tree)
## Zero inflation: ~fn50
## Data: dummy
##
## AIC BIC logLik deviance df.resid
## 987.5 1011.6 -485.7 971.5 143
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Tree (Intercept) 0.6803 0.8248
## Number of obs: 151, groups: Tree, 47
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.463849 0.341347 -1.359 0.1742
## fn50 -0.072480 0.055900 -1.297 0.1948
## fn1400 0.000916 0.011328 0.081 0.9356
## Heightlow 0.229726 0.118437 1.940 0.0524 .
## cec -0.087555 0.201846 -0.434 0.6645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Zero-inflation model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0566 0.2000 -0.283 0.777
## fn50 -0.2262 0.1714 -1.320 0.187| Â | total.V | FD.V | TD.V | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 2.21 | 0.30 – 16.35 | 0.439 | 1.00 | 0.69 – 1.44 | 1.000 | 0.63 | 0.32 – 1.23 | 0.174 |
| fn50 | 0.85 | 0.78 – 0.92 | <0.001 | 1.00 | 0.87 – 1.15 | 0.997 | 0.93 | 0.83 – 1.04 | 0.195 |
| fn1400 | 1.04 | 1.03 – 1.06 | <0.001 | 1.00 | 0.98 – 1.03 | 0.809 | 1.00 | 0.98 – 1.02 | 0.936 |
| Height [low] | 0.58 | 0.53 – 0.65 | <0.001 | 1.00 | 0.77 – 1.29 | 0.989 | 1.26 | 1.00 – 1.59 | 0.052 |
| CanopyDens | 0.99 | 0.96 – 1.01 | 0.234 | 0.99 | 0.99 – 0.99 | <0.001 | |||
| cec | 0.83 | 0.53 – 1.30 | 0.418 | 0.92 | 0.62 – 1.36 | 0.664 | |||
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.35 | 0.22 – 0.56 | <0.001 | 1.00 | 0.89 – 1.12 | 0.997 | 0.94 | 0.64 – 1.40 | 0.777 |
| fn50 | 0.84 | 0.55 – 1.29 | 0.435 | 1.00 | 0.81 – 1.24 | 0.998 | 0.80 | 0.57 – 1.12 | 0.187 |
| Random Effects | |||||||||
| σ2 | 0.71 | 1.83 | 0.55 | ||||||
| Ï„00 | 0.76 Tree | 1.00 Tree | 0.68 Tree | ||||||
| ICC | 0.52 | 0.35 | 0.56 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.114 / 0.572 | 0.007 / 0.358 | 0.017 / 0.563 | ||||||
Outcome table
| X | FN50 | FNTOTAL | Height | FN50.x.FN1400 |
|---|---|---|---|---|
| Total Visitation | NS | .004 / p=0 | -0.054 / p=0 | NS |
| Flying visitiation | -1.02 / p=0 | 0.05 / p=0 | -.196 / p=0 | 0.03 / p= 0.006 |
| non-flying visitation | .61 / p=0.0424 | NS | NS | -0.03 / P=.0207 |
## NOTE: fn1400 is not a high-order term in the model
## NOTE: fn50 is not a high-order term in the model
## NOTE: fn1400 is not a high-order term in the model
## NOTE: fn50 is not a high-order term in the model
## NOTE: fn1400 is not a high-order term in the model
## NOTE: fn50 is not a high-order term in the model
Effects chart
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'